This paper covers Contrastive Unpaired Image-to-Image Translation [1]}, a successor to the CycleGAN [2]} method from 2017 and proposes tweaks to the model that result in notable performance increases.
This new method harnesses the use of generative adversarial networks [3]} while both improving on the training speed and accuracy on certain problems in comparison to other baseline methods (e.g. CycleGAN [2]}, MUNIT [5]}, DRIT [6]}, DistanceGAN and SelfDistance [7]}, and GCGAN [8]}). Generative adversarial networks [3]} have proven to be especially useful in the domain of unsupervised learning i.e. when the data used to train the model is not labeled, which is the exact case in this kind of scenario.
